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DBMS > Kinetica vs. MonetDB vs. Trafodion vs. Vertica

System Properties Comparison Kinetica vs. MonetDB vs. Trafodion vs. Vertica

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonMonetDB  Xexclude from comparisonTrafodion  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.
DescriptionFully vectorized database across both GPUs and CPUsA relational database management system that stores data in columnsTransactional SQL-on-Hadoop DBMSCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMS infoColumn oriented
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Spatial DBMS
Spatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score1.72
Rank#145  Overall
#67  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.kinetica.comwww.monetdb.orgtrafodion.apache.orgwww.vertica.com
Technical documentationdocs.kinetica.comwww.monetdb.org/­Documentationtrafodion.apache.org/­documentation.htmlvertica.com/­documentation
DeveloperKineticaMonetDB BVApache Software Foundation, originally developed by HPOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2012200420142005
Current release7.1, August 2021Dec2023 (11.49), December 20232.3.0, February 201912.0.3, January 2023
License infoCommercial or Open SourcecommercialOpen Source infoMozilla Public License 2.0Open Source infoApache 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++CC++, JavaC++
Server operating systemsLinuxFreeBSD
Linux
OS X
Solaris
Windows
LinuxLinux
Data schemeyesyesyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesyesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infoSQL 2003 with some extensionsyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
ADO.NET
JDBC
ODBC
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
All languages supporting JDBC/ODBC/ADO.NetC#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresuser defined functionsyes, in SQL, C, RJava Stored Proceduresyes, PostgreSQL PL/pgSQL, with minor differences
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangeyesnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding via remote tablesShardinghorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone infoSource-replica replication available in experimental statusyes, via HBaseMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infovia user defined functions and HBaseno infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users and roles on table levelfine grained access rights according to SQL-standardfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
KineticaMonetDBTrafodionVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more
Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more
Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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More resources
KineticaMonetDBTrafodionVertica infoOpenText™ Vertica™
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